Overview

Dataset statistics

Number of variables20
Number of observations388
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory60.8 KiB
Average record size in memory160.3 B

Variable types

Categorical3
Numeric15
Boolean2

Warnings

Churn has constant value "1" Constant
State has a high cardinality: 51 distinct values High cardinality
Total day minutes is highly correlated with Total day chargeHigh correlation
Total day charge is highly correlated with Total day minutesHigh correlation
Total eve minutes is highly correlated with Total eve chargeHigh correlation
Total eve charge is highly correlated with Total eve minutesHigh correlation
Total night minutes is highly correlated with Total night chargeHigh correlation
Total night charge is highly correlated with Total night minutesHigh correlation
Total intl minutes is highly correlated with Total intl chargeHigh correlation
Total intl charge is highly correlated with Total intl minutesHigh correlation
Area code is highly correlated with ChurnHigh correlation
Churn is highly correlated with Area code and 3 other fieldsHigh correlation
International plan is highly correlated with ChurnHigh correlation
State is highly correlated with ChurnHigh correlation
Voice mail plan is highly correlated with ChurnHigh correlation
Number vmail messages has 323 (83.2%) zeros Zeros
Customer service calls has 79 (20.4%) zeros Zeros

Reproduction

Analysis started2021-04-10 21:20:26.785164
Analysis finished2021-04-10 21:20:49.238582
Duration22.45 seconds
Software versionpandas-profiling v2.11.0
Download configurationconfig.yaml

Variables

State
Categorical

HIGH CARDINALITY
HIGH CORRELATION

Distinct51
Distinct (%)13.1%
Missing0
Missing (%)0.0%
Memory size3.2 KiB
TX
 
16
NJ
 
14
MD
 
14
MI
 
13
NV
 
13
Other values (46)
318 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters776
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCO
2nd rowAZ
3rd rowMD
4th rowWY
5th rowCO
ValueCountFrequency (%)
TX16
 
4.1%
NJ14
 
3.6%
MD14
 
3.6%
MI13
 
3.4%
NV13
 
3.4%
MN13
 
3.4%
NY12
 
3.1%
AR11
 
2.8%
CT11
 
2.8%
ME11
 
2.8%
Other values (41)260
67.0%
2021-04-10T16:20:49.417332image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
tx16
 
4.1%
md14
 
3.6%
nj14
 
3.6%
nv13
 
3.4%
mi13
 
3.4%
mn13
 
3.4%
ny12
 
3.1%
sc11
 
2.8%
ar11
 
2.8%
ms11
 
2.8%
Other values (41)260
67.0%

Most occurring characters

ValueCountFrequency (%)
N93
12.0%
M89
 
11.5%
A73
 
9.4%
T56
 
7.2%
C48
 
6.2%
D42
 
5.4%
I40
 
5.2%
S38
 
4.9%
O36
 
4.6%
V30
 
3.9%
Other values (14)231
29.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter776
100.0%

Most frequent character per category

ValueCountFrequency (%)
N93
12.0%
M89
 
11.5%
A73
 
9.4%
T56
 
7.2%
C48
 
6.2%
D42
 
5.4%
I40
 
5.2%
S38
 
4.9%
O36
 
4.6%
V30
 
3.9%
Other values (14)231
29.8%

Most occurring scripts

ValueCountFrequency (%)
Latin776
100.0%

Most frequent character per script

ValueCountFrequency (%)
N93
12.0%
M89
 
11.5%
A73
 
9.4%
T56
 
7.2%
C48
 
6.2%
D42
 
5.4%
I40
 
5.2%
S38
 
4.9%
O36
 
4.6%
V30
 
3.9%
Other values (14)231
29.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII776
100.0%

Most frequent character per block

ValueCountFrequency (%)
N93
12.0%
M89
 
11.5%
A73
 
9.4%
T56
 
7.2%
C48
 
6.2%
D42
 
5.4%
I40
 
5.2%
S38
 
4.9%
O36
 
4.6%
V30
 
3.9%
Other values (14)231
29.8%

Account length
Real number (ℝ≥0)

Distinct154
Distinct (%)39.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean102.3195876
Minimum1
Maximum225
Zeros0
Zeros (%)0.0%
Memory size3.2 KiB
2021-04-10T16:20:49.519477image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile36.35
Q175.75
median103
Q3127
95-th percentile170
Maximum225
Range224
Interquartile range (IQR)51.25

Descriptive statistics

Standard deviation40.18459895
Coefficient of variation (CV)0.3927361308
Kurtosis-0.02151221649
Mean102.3195876
Median Absolute Deviation (MAD)27
Skewness0.09201571449
Sum39700
Variance1614.801993
MonotocityNot monotonic
2021-04-10T16:20:49.624816image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1157
 
1.8%
937
 
1.8%
1137
 
1.8%
887
 
1.8%
767
 
1.8%
1056
 
1.5%
976
 
1.5%
986
 
1.5%
1086
 
1.5%
1195
 
1.3%
Other values (144)324
83.5%
ValueCountFrequency (%)
11
0.3%
21
0.3%
121
0.3%
131
0.3%
161
0.3%
ValueCountFrequency (%)
2251
0.3%
2241
0.3%
2121
0.3%
2011
0.3%
1971
0.3%

Area code
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size3.2 KiB
415
195 
510
99 
408
94 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1164
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row408
2nd row408
3rd row408
4th row415
5th row408
ValueCountFrequency (%)
415195
50.3%
51099
25.5%
40894
24.2%
2021-04-10T16:20:49.823358image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-04-10T16:20:49.887909image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
415195
50.3%
51099
25.5%
40894
24.2%

Most occurring characters

ValueCountFrequency (%)
1294
25.3%
5294
25.3%
4289
24.8%
0193
16.6%
894
 
8.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1164
100.0%

Most frequent character per category

ValueCountFrequency (%)
1294
25.3%
5294
25.3%
4289
24.8%
0193
16.6%
894
 
8.1%

Most occurring scripts

ValueCountFrequency (%)
Common1164
100.0%

Most frequent character per script

ValueCountFrequency (%)
1294
25.3%
5294
25.3%
4289
24.8%
0193
16.6%
894
 
8.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII1164
100.0%

Most frequent character per block

ValueCountFrequency (%)
1294
25.3%
5294
25.3%
4289
24.8%
0193
16.6%
894
 
8.1%

International plan
Boolean

HIGH CORRELATION

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size516.0 B
False
270 
True
118 
ValueCountFrequency (%)
False270
69.6%
True118
30.4%
2021-04-10T16:20:49.925943image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Voice mail plan
Boolean

HIGH CORRELATION

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size516.0 B
False
323 
True
65 
ValueCountFrequency (%)
False323
83.2%
True65
 
16.8%
2021-04-10T16:20:49.958080image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Number vmail messages
Real number (ℝ≥0)

ZEROS

Distinct27
Distinct (%)7.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.170103093
Minimum0
Maximum45
Zeros323
Zeros (%)83.2%
Memory size3.2 KiB
2021-04-10T16:20:50.018860image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile33
Maximum45
Range45
Interquartile range (IQR)0

Descriptive statistics

Standard deviation11.87649332
Coefficient of variation (CV)2.297148259
Kurtosis2.375173698
Mean5.170103093
Median Absolute Deviation (MAD)0
Skewness2.002884776
Sum2006
Variance141.0510935
MonotocityNot monotonic
2021-04-10T16:20:50.115834image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
0323
83.2%
296
 
1.5%
336
 
1.5%
326
 
1.5%
265
 
1.3%
424
 
1.0%
354
 
1.0%
314
 
1.0%
284
 
1.0%
363
 
0.8%
Other values (17)23
 
5.9%
ValueCountFrequency (%)
0323
83.2%
161
 
0.3%
171
 
0.3%
182
 
0.5%
191
 
0.3%
ValueCountFrequency (%)
451
 
0.3%
442
0.5%
424
1.0%
411
 
0.3%
401
 
0.3%

Total day minutes
Real number (ℝ≥0)

HIGH CORRELATION

Distinct365
Distinct (%)94.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean205.1811856
Minimum0
Maximum350.8
Zeros1
Zeros (%)0.3%
Memory size3.2 KiB
2021-04-10T16:20:50.222156image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile97.65
Q1150.9
median214.95
Q3262.2
95-th percentile304.68
Maximum350.8
Range350.8
Interquartile range (IQR)111.3

Descriptive statistics

Standard deviation68.49021343
Coefficient of variation (CV)0.3338035758
Kurtosis-0.751902766
Mean205.1811856
Median Absolute Deviation (MAD)55.05
Skewness-0.1842694097
Sum79610.3
Variance4690.909335
MonotocityNot monotonic
2021-04-10T16:20:50.332253image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
242.22
 
0.5%
131.62
 
0.5%
176.92
 
0.5%
256.42
 
0.5%
189.12
 
0.5%
162.32
 
0.5%
236.92
 
0.5%
162.12
 
0.5%
133.32
 
0.5%
248.72
 
0.5%
Other values (355)368
94.8%
ValueCountFrequency (%)
01
0.3%
46.51
0.3%
47.71
0.3%
47.81
0.3%
54.21
0.3%
ValueCountFrequency (%)
350.81
0.3%
346.81
0.3%
345.31
0.3%
337.41
0.3%
335.51
0.3%

Total day calls
Real number (ℝ≥0)

Distinct96
Distinct (%)24.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean101.1958763
Minimum0
Maximum156
Zeros1
Zeros (%)0.3%
Memory size3.2 KiB
2021-04-10T16:20:50.547577image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile63
Q187
median103
Q3116
95-th percentile134
Maximum156
Range156
Interquartile range (IQR)29

Descriptive statistics

Standard deviation21.70527945
Coefficient of variation (CV)0.2144877859
Kurtosis0.8873766371
Mean101.1958763
Median Absolute Deviation (MAD)14
Skewness-0.4495566036
Sum39264
Variance471.1191561
MonotocityNot monotonic
2021-04-10T16:20:50.661627image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10613
 
3.4%
10812
 
3.1%
11210
 
2.6%
10110
 
2.6%
999
 
2.3%
1039
 
2.3%
839
 
2.3%
869
 
2.3%
1209
 
2.3%
1098
 
2.1%
Other values (86)290
74.7%
ValueCountFrequency (%)
01
0.3%
421
0.3%
442
0.5%
451
0.3%
471
0.3%
ValueCountFrequency (%)
1561
0.3%
1512
0.5%
1481
0.3%
1472
0.5%
1451
0.3%

Total day charge
Real number (ℝ≥0)

HIGH CORRELATION

Distinct365
Distinct (%)94.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.88134021
Minimum0
Maximum59.64
Zeros1
Zeros (%)0.3%
Memory size3.2 KiB
2021-04-10T16:20:50.783835image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile16.5985
Q125.6525
median36.54
Q344.5775
95-th percentile51.7965
Maximum59.64
Range59.64
Interquartile range (IQR)18.925

Descriptive statistics

Standard deviation11.64347874
Coefficient of variation (CV)0.333802505
Kurtosis-0.7518204735
Mean34.88134021
Median Absolute Deviation (MAD)9.36
Skewness-0.1842373786
Sum13533.96
Variance135.5705972
MonotocityNot monotonic
2021-04-10T16:20:50.893307image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
41.682
 
0.5%
48.692
 
0.5%
24.652
 
0.5%
46.092
 
0.5%
28.412
 
0.5%
27.592
 
0.5%
22.662
 
0.5%
43.592
 
0.5%
22.372
 
0.5%
46.362
 
0.5%
Other values (355)368
94.8%
ValueCountFrequency (%)
01
0.3%
7.911
0.3%
8.111
0.3%
8.131
0.3%
9.211
0.3%
ValueCountFrequency (%)
59.641
0.3%
58.961
0.3%
58.71
0.3%
57.361
0.3%
57.041
0.3%

Total eve minutes
Real number (ℝ≥0)

HIGH CORRELATION

Distinct349
Distinct (%)89.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean209.3853093
Minimum70.9
Maximum363.7
Zeros0
Zeros (%)0.0%
Memory size3.2 KiB
2021-04-10T16:20:51.019588image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum70.9
5-th percentile126.675
Q1173.15
median209
Q3248.325
95-th percentile287.755
Maximum363.7
Range292.8
Interquartile range (IQR)75.175

Descriptive statistics

Standard deviation50.86371836
Coefficient of variation (CV)0.2429192312
Kurtosis-0.09507014465
Mean209.3853093
Median Absolute Deviation (MAD)37.35
Skewness0.03540140643
Sum81241.5
Variance2587.117846
MonotocityNot monotonic
2021-04-10T16:20:51.135405image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
209.43
 
0.8%
226.13
 
0.8%
169.92
 
0.5%
253.42
 
0.5%
208.92
 
0.5%
134.12
 
0.5%
188.82
 
0.5%
303.42
 
0.5%
1902
 
0.5%
179.32
 
0.5%
Other values (339)366
94.3%
ValueCountFrequency (%)
70.91
0.3%
75.31
0.3%
77.11
0.3%
92.31
0.3%
93.71
0.3%
ValueCountFrequency (%)
363.71
0.3%
350.91
0.3%
347.31
0.3%
339.91
0.3%
3271
0.3%

Total eve calls
Real number (ℝ≥0)

Distinct87
Distinct (%)22.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean99.94845361
Minimum48
Maximum159
Zeros0
Zeros (%)0.0%
Memory size3.2 KiB
2021-04-10T16:20:51.265855image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum48
5-th percentile67
Q186
median100.5
Q3113
95-th percentile132
Maximum159
Range111
Interquartile range (IQR)27

Descriptive statistics

Standard deviation19.60547365
Coefficient of variation (CV)0.1961558478
Kurtosis-0.2661983882
Mean99.94845361
Median Absolute Deviation (MAD)13.5
Skewness-0.06270721028
Sum38780
Variance384.3745971
MonotocityNot monotonic
2021-04-10T16:20:51.376709image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9412
 
3.1%
10212
 
3.1%
11111
 
2.8%
1009
 
2.3%
1089
 
2.3%
1179
 
2.3%
869
 
2.3%
929
 
2.3%
1059
 
2.3%
1228
 
2.1%
Other values (77)291
75.0%
ValueCountFrequency (%)
482
0.5%
531
0.3%
541
0.3%
562
0.5%
591
0.3%
ValueCountFrequency (%)
1591
0.3%
1471
0.3%
1441
0.3%
1431
0.3%
1421
0.3%

Total eve charge
Real number (ℝ≥0)

HIGH CORRELATION

Distinct338
Distinct (%)87.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.79786082
Minimum6.03
Maximum30.91
Zeros0
Zeros (%)0.0%
Memory size3.2 KiB
2021-04-10T16:20:51.491497image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum6.03
5-th percentile10.767
Q114.7175
median17.765
Q321.11
95-th percentile24.459
Maximum30.91
Range24.88
Interquartile range (IQR)6.3925

Descriptive statistics

Standard deviation4.323326587
Coefficient of variation (CV)0.242912709
Kurtosis-0.09500132295
Mean17.79786082
Median Absolute Deviation (MAD)3.175
Skewness0.03542926839
Sum6905.57
Variance18.69115278
MonotocityNot monotonic
2021-04-10T16:20:51.612814image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17.714
 
1.0%
17.83
 
0.8%
14.23
 
0.8%
19.223
 
0.8%
17.222
 
0.5%
15.562
 
0.5%
16.152
 
0.5%
22.072
 
0.5%
25.792
 
0.5%
15.242
 
0.5%
Other values (328)363
93.6%
ValueCountFrequency (%)
6.031
0.3%
6.41
0.3%
6.551
0.3%
7.851
0.3%
7.961
0.3%
ValueCountFrequency (%)
30.911
0.3%
29.831
0.3%
29.521
0.3%
28.891
0.3%
27.81
0.3%

Total night minutes
Real number (ℝ≥0)

HIGH CORRELATION

Distinct349
Distinct (%)89.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean205.3072165
Minimum47.4
Maximum354.9
Zeros0
Zeros (%)0.0%
Memory size3.2 KiB
2021-04-10T16:20:51.722039image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum47.4
5-th percentile129.25
Q1169.925
median204.95
Q3241.15
95-th percentile280.695
Maximum354.9
Range307.5
Interquartile range (IQR)71.225

Descriptive statistics

Standard deviation47.56515726
Coefficient of variation (CV)0.2316779608
Kurtosis-0.1882849493
Mean205.3072165
Median Absolute Deviation (MAD)35.9
Skewness0.01552955433
Sum79659.2
Variance2262.444186
MonotocityNot monotonic
2021-04-10T16:20:51.829601image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
178.13
 
0.8%
2273
 
0.8%
214.23
 
0.8%
167.82
 
0.5%
208.92
 
0.5%
184.22
 
0.5%
1952
 
0.5%
254.92
 
0.5%
249.42
 
0.5%
153.22
 
0.5%
Other values (339)365
94.1%
ValueCountFrequency (%)
47.41
0.3%
73.21
0.3%
87.41
0.3%
104.91
0.3%
107.31
0.3%
ValueCountFrequency (%)
354.91
0.3%
332.71
0.3%
321.21
0.3%
309.11
0.3%
308.91
0.3%

Total night calls
Real number (ℝ≥0)

Distinct94
Distinct (%)24.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean100.6829897
Minimum49
Maximum158
Zeros0
Zeros (%)0.0%
Memory size3.2 KiB
2021-04-10T16:20:52.057444image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum49
5-th percentile69.35
Q185.75
median101
Q3116
95-th percentile132
Maximum158
Range109
Interquartile range (IQR)30.25

Descriptive statistics

Standard deviation20.07466732
Coefficient of variation (CV)0.1993848949
Kurtosis-0.3220498253
Mean100.6829897
Median Absolute Deviation (MAD)15
Skewness0.04762314947
Sum39065
Variance402.992268
MonotocityNot monotonic
2021-04-10T16:20:52.171504image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10611
 
2.8%
9710
 
2.6%
8410
 
2.6%
789
 
2.3%
1029
 
2.3%
1049
 
2.3%
959
 
2.3%
1119
 
2.3%
1189
 
2.3%
1158
 
2.1%
Other values (84)295
76.0%
ValueCountFrequency (%)
491
0.3%
511
0.3%
531
0.3%
561
0.3%
571
0.3%
ValueCountFrequency (%)
1581
0.3%
1522
0.5%
1511
0.3%
1471
0.3%
1461
0.3%

Total night charge
Real number (ℝ≥0)

HIGH CORRELATION

Distinct289
Distinct (%)74.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.238891753
Minimum2.13
Maximum15.97
Zeros0
Zeros (%)0.0%
Memory size3.2 KiB
2021-04-10T16:20:52.279874image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum2.13
5-th percentile5.8175
Q17.6475
median9.225
Q310.8525
95-th percentile12.633
Maximum15.97
Range13.84
Interquartile range (IQR)3.205

Descriptive statistics

Standard deviation2.140617182
Coefficient of variation (CV)0.2316963159
Kurtosis-0.1877677769
Mean9.238891753
Median Absolute Deviation (MAD)1.615
Skewness0.0151953845
Sum3584.69
Variance4.582241921
MonotocityNot monotonic
2021-04-10T16:20:52.405372image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8.014
 
1.0%
9.644
 
1.0%
9.44
 
1.0%
10.224
 
1.0%
8.193
 
0.8%
8.883
 
0.8%
12.113
 
0.8%
7.553
 
0.8%
10.523
 
0.8%
10.083
 
0.8%
Other values (279)354
91.2%
ValueCountFrequency (%)
2.131
0.3%
3.291
0.3%
3.931
0.3%
4.721
0.3%
4.831
0.3%
ValueCountFrequency (%)
15.971
0.3%
14.971
0.3%
14.451
0.3%
13.911
0.3%
13.91
0.3%

Total intl minutes
Real number (ℝ≥0)

HIGH CORRELATION

Distinct111
Distinct (%)28.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.8193299
Minimum3.9
Maximum20
Zeros0
Zeros (%)0.0%
Memory size3.2 KiB
2021-04-10T16:20:52.526897image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum3.9
5-th percentile6.2
Q18.9
median10.8
Q312.925
95-th percentile14.8
Maximum20
Range16.1
Interquartile range (IQR)4.025

Descriptive statistics

Standard deviation2.771824382
Coefficient of variation (CV)0.2561918721
Kurtosis-0.1462839677
Mean10.8193299
Median Absolute Deviation (MAD)2
Skewness0.02455527575
Sum4197.9
Variance7.683010403
MonotocityNot monotonic
2021-04-10T16:20:52.639362image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13.911
 
2.8%
11.59
 
2.3%
11.19
 
2.3%
118
 
2.1%
10.17
 
1.8%
107
 
1.8%
9.67
 
1.8%
7.97
 
1.8%
13.37
 
1.8%
11.37
 
1.8%
Other values (101)309
79.6%
ValueCountFrequency (%)
3.91
0.3%
4.12
0.5%
4.21
0.3%
4.52
0.5%
4.71
0.3%
ValueCountFrequency (%)
201
0.3%
17.91
0.3%
17.61
0.3%
17.51
0.3%
17.31
0.3%

Total intl calls
Real number (ℝ≥0)

Distinct16
Distinct (%)4.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.051546392
Minimum1
Maximum20
Zeros0
Zeros (%)0.0%
Memory size3.2 KiB
2021-04-10T16:20:52.742180image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q35
95-th percentile9
Maximum20
Range19
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.468912173
Coefficient of variation (CV)0.6093752692
Kurtosis6.582263802
Mean4.051546392
Median Absolute Deviation (MAD)1
Skewness1.923546794
Sum1572
Variance6.095527318
MonotocityNot monotonic
2021-04-10T16:20:52.823348image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
286
22.2%
384
21.6%
465
16.8%
542
10.8%
635
9.0%
126
 
6.7%
721
 
5.4%
910
 
2.6%
88
 
2.1%
103
 
0.8%
Other values (6)8
 
2.1%
ValueCountFrequency (%)
126
 
6.7%
286
22.2%
384
21.6%
465
16.8%
542
10.8%
ValueCountFrequency (%)
201
0.3%
152
0.5%
141
0.3%
131
0.3%
121
0.3%

Total intl charge
Real number (ℝ≥0)

HIGH CORRELATION

Distinct111
Distinct (%)28.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.921726804
Minimum1.05
Maximum5.4
Zeros0
Zeros (%)0.0%
Memory size3.2 KiB
2021-04-10T16:20:52.936203image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1.05
5-th percentile1.67
Q12.4
median2.92
Q33.4875
95-th percentile4
Maximum5.4
Range4.35
Interquartile range (IQR)1.0875

Descriptive statistics

Standard deviation0.7484308185
Coefficient of variation (CV)0.2561604382
Kurtosis-0.147718281
Mean2.921726804
Median Absolute Deviation (MAD)0.54
Skewness0.02415757327
Sum1133.63
Variance0.56014869
MonotocityNot monotonic
2021-04-10T16:20:53.045054image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.7511
 
2.8%
3.119
 
2.3%
39
 
2.3%
2.978
 
2.1%
3.597
 
1.8%
2.137
 
1.8%
2.597
 
1.8%
3.057
 
1.8%
2.737
 
1.8%
2.77
 
1.8%
Other values (101)309
79.6%
ValueCountFrequency (%)
1.051
0.3%
1.112
0.5%
1.131
0.3%
1.222
0.5%
1.271
0.3%
ValueCountFrequency (%)
5.41
0.3%
4.831
0.3%
4.751
0.3%
4.731
0.3%
4.671
0.3%

Customer service calls
Real number (ℝ≥0)

ZEROS

Distinct10
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.206185567
Minimum0
Maximum9
Zeros79
Zeros (%)20.4%
Memory size3.2 KiB
2021-04-10T16:20:53.142240image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q34
95-th percentile5
Maximum9
Range9
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.882535781
Coefficient of variation (CV)0.8532989289
Kurtosis0.03255737131
Mean2.206185567
Median Absolute Deviation (MAD)1
Skewness0.7460275042
Sum856
Variance3.543940968
MonotocityNot monotonic
2021-04-10T16:20:53.212064image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
199
25.5%
079
20.4%
464
16.5%
262
16.0%
337
 
9.5%
529
 
7.5%
610
 
2.6%
75
 
1.3%
92
 
0.5%
81
 
0.3%
ValueCountFrequency (%)
079
20.4%
199
25.5%
262
16.0%
337
 
9.5%
464
16.5%
ValueCountFrequency (%)
92
 
0.5%
81
 
0.3%
75
 
1.3%
610
 
2.6%
529
7.5%

Churn
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size3.2 KiB
1
388 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters388
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1
ValueCountFrequency (%)
1388
100.0%
2021-04-10T16:20:53.374590image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-04-10T16:20:53.442746image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
1388
100.0%

Most occurring characters

ValueCountFrequency (%)
1388
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number388
100.0%

Most frequent character per category

ValueCountFrequency (%)
1388
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common388
100.0%

Most frequent character per script

ValueCountFrequency (%)
1388
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII388
100.0%

Most frequent character per block

ValueCountFrequency (%)
1388
100.0%

Interactions

2021-04-10T16:20:27.652377image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:20:27.845492image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:20:27.938448image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:20:28.041348image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:20:28.129900image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:20:28.215536image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:20:28.309183image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:20:28.403224image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:20:28.501119image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:20:28.586254image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:20:28.674615image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:20:28.767560image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:20:28.858553image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:20:28.939474image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:20:29.021722image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:20:29.120653image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:20:29.211405image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:20:29.305796image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:20:29.450746image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:20:29.544136image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:20:29.630820image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:20:29.716145image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:20:29.804512image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:20:29.893941image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:20:29.986587image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:20:30.082619image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:20:30.177976image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:20:30.269060image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:20:30.444361image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:20:30.540195image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:20:30.638448image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:20:30.739720image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:20:30.845637image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:20:30.946281image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:20:31.039145image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:20:31.140300image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:20:31.237300image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:20:31.347240image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:20:31.448022image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:20:31.548460image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:20:31.648662image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:20:31.743739image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:20:31.843185image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:20:31.938693image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:20:32.036716image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:20:32.138716image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:20:32.237087image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:20:32.335473image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:20:32.440106image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:20:32.553219image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:20:32.669283image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:20:32.768317image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:20:32.877308image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:20:33.085527image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:20:33.194404image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:20:33.292284image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:20:33.388709image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:20:33.481098image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:20:33.579678image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:20:33.681024image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:20:33.781059image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:20:33.875725image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:20:33.975827image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:20:34.068038image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:20:34.164517image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:20:34.259055image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:20:34.361712image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:20:34.462712image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:20:34.562712image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:20:34.664121image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:20:34.779073image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:20:34.897946image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:20:34.997212image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:20:35.095245image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:20:35.196359image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:20:35.292483image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:20:35.383483image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:20:35.475482image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:20:35.568651image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:20:35.784257image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:20:35.877717image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:20:35.972786image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:20:36.066249image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:20:36.153423image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:20:36.246030image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:20:36.333172image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:20:36.418562image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:20:36.511945image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:20:36.606581image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:20:36.699343image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:20:36.789545image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:20:36.908329image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:20:37.003182image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:20:37.091220image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:20:37.185492image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:20:37.280493image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:20:37.381135image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:20:37.469723image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:20:37.555147image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:20:37.640547image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:20:37.724808image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:20:37.815135image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:20:37.911651image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:20:38.011296image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:20:38.107260image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:20:38.293261image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:20:38.387014image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:20:38.478657image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:20:38.570013image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:20:38.664234image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:20:38.760330image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:20:38.848824image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:20:38.940796image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:20:39.033944image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:20:39.135195image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:20:39.231468image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:20:39.329216image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:20:39.422345image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:20:39.519176image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:20:39.613764image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:20:39.703384image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:20:39.794410image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:20:39.890447image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:20:39.988371image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:20:40.085206image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:20:40.188504image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:20:40.286019image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:20:40.372368image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:20:40.471110image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:20:40.569465image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:20:40.664561image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:20:40.867763image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:20:40.958764image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:20:41.047842image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:20:41.135567image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:20:41.226721image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:20:41.324905image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:20:41.418381image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:20:41.513956image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:20:41.597852image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:20:41.685616image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:20:41.778541image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:20:41.875964image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:20:41.976483image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:20:42.077518image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:20:42.172552image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:20:42.269307image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:20:42.366715image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:20:42.471197image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:20:42.567351image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:20:42.658961image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:20:42.761269image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:20:42.868126image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:20:42.960267image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:20:43.050747image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:20:43.145707image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:20:43.239510image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:20:43.443438image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:20:43.572823image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:20:43.672077image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:20:43.770352image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:20:43.872738image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:20:43.971967image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:20:44.074064image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:20:44.171099image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:20:44.271210image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:20:44.375128image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:20:44.473387image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:20:44.572730image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:20:44.675097image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:20:44.768929image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:20:44.871218image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:20:44.973878image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:20:45.071079image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:20:45.166410image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:20:45.264379image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:20:45.362480image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:20:45.462485image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:20:45.559485image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:20:45.658968image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:20:45.770264image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:20:45.862514image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:20:46.063707image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:20:46.147958image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:20:46.230332image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:20:46.318691image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:20:46.408928image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:20:46.497437image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:20:46.592948image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:20:46.678948image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:20:46.766957image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:20:46.872980image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:20:46.956542image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:20:47.043384image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:20:47.137024image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:20:47.227642image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:20:47.307998image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:20:47.389343image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:20:47.472324image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:20:47.560564image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:20:47.653182image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:20:47.739195image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:20:47.823380image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:20:47.925325image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:20:48.018010image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:20:48.106071image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:20:48.194164image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:20:48.292375image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:20:48.381301image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:20:48.567800image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Correlations

2021-04-10T16:20:53.517366image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-04-10T16:20:53.850996image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-04-10T16:20:54.086030image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-04-10T16:20:54.327128image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-04-10T16:20:54.551545image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-04-10T16:20:48.760957image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
A simple visualization of nullity by column.
2021-04-10T16:20:49.115409image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

StateAccount lengthArea codeInternational planVoice mail planNumber vmail messagesTotal day minutesTotal day callsTotal day chargeTotal eve minutesTotal eve callsTotal eve chargeTotal night minutesTotal night callsTotal night chargeTotal intl minutesTotal intl callsTotal intl chargeCustomer service callsChurn
0CO77408NoNo062.48910.61169.912114.44209.6649.435.761.5451
1AZ12408NoNo0249.611842.43252.411921.45280.29012.6111.833.1911
2MD135408YesYes41173.18529.43203.910717.33122.2785.5014.6153.9401
3WY87415NoNo0151.08325.67219.711618.67203.91279.189.732.6251
4CO121408NoYes30198.412933.7375.3776.40181.2778.155.831.5731
5TX150510NoNo0178.910130.41169.111014.37148.61006.6913.833.7341
6DC82415NoNo0300.310951.05181.010015.39270.17312.1511.743.1601
7NY144408NoNo061.611710.4777.1856.55173.0997.798.272.2141
8TX106510NoNo0210.69635.80249.28521.18191.4888.6112.413.3521
9IN94408NoNo0157.910526.84155.010113.18189.6848.538.052.1641

Last rows

StateAccount lengthArea codeInternational planVoice mail planNumber vmail messagesTotal day minutesTotal day callsTotal day chargeTotal eve minutesTotal eve callsTotal eve chargeTotal night minutesTotal night callsTotal night chargeTotal intl minutesTotal intl callsTotal intl chargeCustomer service callsChurn
378OK146510NoNo0138.410423.53158.912213.5147.4732.133.991.0541
379RI138510YesNo0286.26148.65187.26015.91146.21146.5811.042.9721
380ID82415NoNo0266.98345.37229.77419.52251.79911.3311.062.9731
381AR76408NoNo0107.314018.24238.213320.25271.811612.2310.032.7041
382KS170415NoYes42199.511933.92135.09011.48184.6498.3110.932.9441
383MI119510YesYes22172.111929.26223.613319.01150.0946.7513.9203.7511
384IL71510YesNo0186.111431.64198.614016.88206.5809.2913.853.7341
385GA122510YesNo0140.010123.80196.47716.69120.11335.409.742.6241
386MD62408NoNo0321.110554.59265.512222.57180.5728.1211.523.1141
387IN117415NoNo0118.412620.13249.39721.19227.05610.2213.633.6751